Detecting the movement and/or position of a subject is important in several healthcare applications. For example, it is often desired to prevent patients in hospital beds from moving in certain ways. As a result of medications, impaired memory, old age and/or other disabilities, patients who attempt to leave their beds without assistance often fall and injure themselves. Unassisted movement of a patient can also lead to medical devices attached to that patient becoming dislodged and ceasing to function properly.
However; the monitoring of patients who should not get out of bed without clinical assistance can place a significant burden on hospital staff.
Many current methods used for bed occupancy detection utilize a camera directed at the bed. In some examples a person (e.g. a medical professional) must constantly monitor the image feed from the camera. In other examples, such as the system described in US 2009/0278934, automated image analysis is used to detect bed exit events, but this requires complicated algorithms for detecting bed boundaries and classifying movements of the subject. Such algorithms can be confused by movements other than that of the subject (e.g. a blanket falling off the bed), leading to false alarms being raised when the subject is not attempting to exit the bed. Also, automated camera-based systems require recalibrating each time the relative position of the camera and the bed changes, and become unreliable if such recalibrations are not performed.
There is therefore a need for a system which can reliably monitor, with minimal input from medical staff, subjects occupying hospital beds and/or other items of furniture to detect when a subject attempts to leave a bed or other item of furniture.